Unenhanced Sparse Vector-based Embedding Method for Sentiment Analysis
Received: 31 December 2024 | Revised: 23 January 2025 | Accepted: 7 February 2025 | Online: 3 April 2025
Corresponding author: B. S. Harish
Abstract
Natural language processing is one of the most trending fields in research, with sentiment analysis being one of the well-known problems in the field. Many methods have been proposed to handle text-based sentiment data, with social networks acting as one of the main data sources and research targets. An important step in designing a text-based model is the embedding method, which helps in the representation of the inputs. This study presents a novel static text embedding method to represent text inputs and compares its sentiment classification performance with some well-known text embedding methods. The results are on par with existing embedding methods, achieving a promising classification accuracy of 90.66%.
Keywords:
sentiment analysis, representation, word2vec, fast-text, word embedding, sparse vector, contextual embedding modelDownloads
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Copyright (c) 2025 G. R. Kishore, B. S. Harish, C. K. Roopa
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